publication . Article . 2017

Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry

Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Leporé, Natasha;
Open Access
  • Published: 09 Jun 2017 Journal: Brain and Behavior, volume 7, page e00733 (issn: 2162-3279, Copyright policy)
  • Publisher: Wiley
Abstract
Introduction Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. Methods Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able t...
Subjects
free text keywords: Original Research, Alzheimer's Disease, dementia, hippocampus, machine learning, multi‐task learning, tensor‐based morphometry
Funded by
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
84 references, page 1 of 6

Barber, R., Ballard, C., McKeith, I. G., Gholkar, A., & O’ Brien, J. T. (2000). MRI volumetric study of dementia with Lewy bodies A comparison with AD and vascular dementia. Neurology, 54(6), 1304–1309.10746602 [PubMed]

Baron, J. C., Chetelat, G., Desgranges, B., & Perchey, G. (2001). In vivo mapping of gray matter loss with voxel‐based morphometry in Mild Alzheimer's Disease. NeuroImage, 14(2), 298–309.11467904 [PubMed]

Becker, G. A., Ichise, M., Barthel, H., Luthardt, J., Patt, M., Seese, A., … Sabri, O. (2013). PET quantification of 18F‐florbetaben binding to β‐amyloid deposits in human brains. Journal of Nuclear Medicine, 54(5), 723–731.23471310 [PubMed]

Blennow, K., & Zetterberg, H. (2013). The application of cerebrospinal fluid biomarkers in early diagnosis of Alzheimer disease. Medical Clinics of North America, 97, 369–376.23642576 [PubMed]

Bozzali, M., Falini, A., & Franceschi, M. (2002). White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. Journal of Neurology, Neurosurgery & Psychiatry, 72(6), 742–746.

Bozzali, M., Franceschi, M., Falini, A., & Pontesilli, S. (2001). Quantification of tissue damage in AD using diffusion tensor and magnetization transfer MRI. Neurology, 57(6), 1135–1137.11571355 [PubMed]

Caselli, R. J., Locke, D. E. C., Dueck, A. C., Knopman, D. S., Woodruff, B. K., Hoffman‐Snyder, C., … Reiman, E. M. (2013). The neuropsychology of normal aging and preclinical Alzheimer's disease. Alzheimer's & Dementia: the Journal of the Alzheimer's Association, 10(1), 84–92.

Choi, S. J., Lim, K. O., & Monteiro, I. (2005). Diffusion tensor imaging of frontal white matter microstructure in early Alzheimer's disease: A preliminary study. Journal of Geriatric Psychiatry and Neurology, 18, 12–19.15681623 [PubMed]

Chua, T. C., Wen, W., & Slavin, M. J. (2008). Diffusion tensor imaging i n mild cognitive impairment and Alzheimer's disease: A review. Current Opinion in Neurology, 21, 83–92.18180656 [PubMed]

Clerx, L., Visser, P. J., Verhey, F., & Aalten, P. (2012). New MRI markers for Alzheimer's disease: A meta‐analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. Journal of Alzheimer's Disease, 29, 405–429.

Concha, L., Gross, D., & Beaulieu, C. (2005). Diffusion tensor tractography of the limbic system. American Journal of Neuroradiology, 26, 2267–2274.16219832 [PubMed]

Davatzikos, C., Fan, Y., Wu, X., Shen, D., & Resnick, S. M. (2008). Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiology of Aging, 29, 514–523.17174012 [OpenAIRE] [PubMed]

De Jong, L. W., Van der Hiele, K., Veer, I. M., & Houwing, J. J. (2008). Strongly reduced volumes of putamen and thalamus in Alzheimer's disease: An MRI study. Brain, 131(12), 3277–3285.19022861 [OpenAIRE] [PubMed]

Douaud, G., Jbabdi, S., Behrens, T. E. J., Menke, R. A., Gass, A., Monsch, A. U., … Smith, S. (2011). DTI measures in crossing‐fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. NeuroImage, 55, 880–890.21182970 [PubMed]

Elias‐Sonnenschein, L. S., Helisalmi, S., Natunen, T., Hall, A., Paajanen, T., Herukka, S.‐K., … Hiltunen, M. (2013). Genetic loci associated with Alzheimer's disease and cerebrospinal fluid biomarkers in a Finnish case‐control cohort. PLoS ONE, 8, e59676.23573206 [OpenAIRE] [PubMed]

84 references, page 1 of 6
Abstract
Introduction Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. Methods Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able t...
Subjects
free text keywords: Original Research, Alzheimer's Disease, dementia, hippocampus, machine learning, multi‐task learning, tensor‐based morphometry
Funded by
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
84 references, page 1 of 6

Barber, R., Ballard, C., McKeith, I. G., Gholkar, A., & O’ Brien, J. T. (2000). MRI volumetric study of dementia with Lewy bodies A comparison with AD and vascular dementia. Neurology, 54(6), 1304–1309.10746602 [PubMed]

Baron, J. C., Chetelat, G., Desgranges, B., & Perchey, G. (2001). In vivo mapping of gray matter loss with voxel‐based morphometry in Mild Alzheimer's Disease. NeuroImage, 14(2), 298–309.11467904 [PubMed]

Becker, G. A., Ichise, M., Barthel, H., Luthardt, J., Patt, M., Seese, A., … Sabri, O. (2013). PET quantification of 18F‐florbetaben binding to β‐amyloid deposits in human brains. Journal of Nuclear Medicine, 54(5), 723–731.23471310 [PubMed]

Blennow, K., & Zetterberg, H. (2013). The application of cerebrospinal fluid biomarkers in early diagnosis of Alzheimer disease. Medical Clinics of North America, 97, 369–376.23642576 [PubMed]

Bozzali, M., Falini, A., & Franceschi, M. (2002). White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. Journal of Neurology, Neurosurgery & Psychiatry, 72(6), 742–746.

Bozzali, M., Franceschi, M., Falini, A., & Pontesilli, S. (2001). Quantification of tissue damage in AD using diffusion tensor and magnetization transfer MRI. Neurology, 57(6), 1135–1137.11571355 [PubMed]

Caselli, R. J., Locke, D. E. C., Dueck, A. C., Knopman, D. S., Woodruff, B. K., Hoffman‐Snyder, C., … Reiman, E. M. (2013). The neuropsychology of normal aging and preclinical Alzheimer's disease. Alzheimer's & Dementia: the Journal of the Alzheimer's Association, 10(1), 84–92.

Choi, S. J., Lim, K. O., & Monteiro, I. (2005). Diffusion tensor imaging of frontal white matter microstructure in early Alzheimer's disease: A preliminary study. Journal of Geriatric Psychiatry and Neurology, 18, 12–19.15681623 [PubMed]

Chua, T. C., Wen, W., & Slavin, M. J. (2008). Diffusion tensor imaging i n mild cognitive impairment and Alzheimer's disease: A review. Current Opinion in Neurology, 21, 83–92.18180656 [PubMed]

Clerx, L., Visser, P. J., Verhey, F., & Aalten, P. (2012). New MRI markers for Alzheimer's disease: A meta‐analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. Journal of Alzheimer's Disease, 29, 405–429.

Concha, L., Gross, D., & Beaulieu, C. (2005). Diffusion tensor tractography of the limbic system. American Journal of Neuroradiology, 26, 2267–2274.16219832 [PubMed]

Davatzikos, C., Fan, Y., Wu, X., Shen, D., & Resnick, S. M. (2008). Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiology of Aging, 29, 514–523.17174012 [OpenAIRE] [PubMed]

De Jong, L. W., Van der Hiele, K., Veer, I. M., & Houwing, J. J. (2008). Strongly reduced volumes of putamen and thalamus in Alzheimer's disease: An MRI study. Brain, 131(12), 3277–3285.19022861 [OpenAIRE] [PubMed]

Douaud, G., Jbabdi, S., Behrens, T. E. J., Menke, R. A., Gass, A., Monsch, A. U., … Smith, S. (2011). DTI measures in crossing‐fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. NeuroImage, 55, 880–890.21182970 [PubMed]

Elias‐Sonnenschein, L. S., Helisalmi, S., Natunen, T., Hall, A., Paajanen, T., Herukka, S.‐K., … Hiltunen, M. (2013). Genetic loci associated with Alzheimer's disease and cerebrospinal fluid biomarkers in a Finnish case‐control cohort. PLoS ONE, 8, e59676.23573206 [OpenAIRE] [PubMed]

84 references, page 1 of 6
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